9844 papers
This paper introduces AIS-TGNN, a framework that combines Temporal Graph Attention Networks with structured Large Language Models to predict port congestion using AIS data while generating operationally interpretable, evidence-grounded natural language explanations that maintain high predictive accuracy and directional consistency.
This paper investigates the generalization capabilities of a multimodal foundation model fine-tuned on diverse synthetic interactive data for the novel task of Open-Set Corrective Assistance, demonstrating that effective open-set assistive intelligence requires datasets encompassing multimodal grounding, defect inference, and exposure to varied scenarios.
The paper proposes VISA, a closed-loop framework that utilizes Group Relative Policy Optimization to inject fine-grained human values into Large Language Models while preserving semantic integrity and mitigating the alignment tax typically caused by standard fine-tuning.
This paper introduces a multilevel training framework for Kolmogorov-Arnold Networks (KANs) that leverages their structural equivalence to multichannel MLPs and the properties of spline basis functions to create a properly nested hierarchy of models, resulting in orders-of-magnitude improvements in training accuracy and speed, particularly for physics-informed neural networks.
SCoUT is a scalable multi-agent reinforcement learning framework that improves coordination by dynamically grouping agents into latent clusters for efficient value estimation and employing counterfactual advantages to precisely guide decentralized communication decisions on when and whom to message.
This paper introduces the Dynamic Behavioral Constraint (DBC) benchmark, a model-agnostic, inference-time governance framework that demonstrates a 36.8% relative reduction in risk exposure and enhanced EU AI Act compliance across multiple LLM families compared to standard safety prompts, validated through a rigorous, taxonomy-driven red-teaming protocol.
This paper presents a novel framework for the simultaneous acquisition of real-time MRI, EEG, and surface EMG to capture brain, muscle, and articulatory activity during speech, featuring a specialized artifact suppression pipeline to overcome technical challenges and enable unprecedented insights into speech neuroscience.
This paper introduces a training-free, non-parametric approach to multi-step theorem prediction that overcomes the scalability limitations of vanilla in-context learning by leveraging Theorem Precedence Graphs to encode temporal dependencies and impose topological constraints, achieving state-of-the-art accuracy on the FormalGeo7k benchmark without gradient-based optimization.
This paper introduces ReCouPLe, a lightweight framework that leverages natural language rationales as causal guidance to train reward models that are robust to spurious correlations and capable of zero-shot transfer to novel tasks, significantly outperforming baselines in reward accuracy and downstream policy performance under distribution shifts.
K-Gen is an interpretable multimodal framework that leverages Multimodal Large Language Models to generate reasoning-guided keypoints from rasterized maps and text, which are then refined into realistic trajectories, outperforming existing baselines on autonomous driving benchmarks.
SEA-TS is a self-evolving agent framework that autonomously generates and optimizes time series forecasting code through innovations like Metric-Advantage MCTS and global steerable reasoning, achieving superior accuracy and discovering novel algorithmic patterns that outperform state-of-the-art methods and human-engineered baselines.
This paper presents a scalable, interpretable framework that achieves 80.4% accuracy in classifying eight professional baseball pitch types using only monocular 3D body kinematics, revealing that upper-body mechanics—particularly wrist position and trunk tilt—are the primary predictors while establishing an empirical ceiling for grip-based distinctions.
This paper proposes DeformTrace, a hybrid architecture combining State Space Models with deformable dynamics and relay tokens to achieve state-of-the-art temporal forgery localization by addressing challenges in boundary ambiguity, sparse forgeries, and long-range modeling.
To address the fidelity-efficiency dilemma in infinite-horizon dialogue streams where existing memory mechanisms fail to support ad-hoc recall, this paper introduces STEM-Bench, a new benchmark for evaluation, and ProStream, a proactive hierarchical memory framework that achieves bounded-state inference with high reasoning fidelity through multi-granular distillation and adaptive spatiotemporal optimization.
FedAFD is a unified multimodal federated learning framework that enhances both client and server performance by employing a bi-level adversarial alignment and granularity-aware fusion for personalized local learning, alongside a similarity-guided ensemble distillation mechanism to effectively handle model heterogeneity and modality discrepancies.
This paper proposes a training-free, low-cost intervention for Diffusion Language Models that sequentially repels intermediate samples in a batch to enhance generative diversity and improve Pass@ performance on reasoning tasks without significant computational overhead.
This paper introduces DP-MTV, the first framework that enables differentially private many-shot multimodal in-context learning for vision-language models by aggregating hundreds of demonstrations into compact task vectors, thereby overcoming the token-scaling limitations of existing methods while preserving significant performance gains under strict privacy constraints.
This paper proposes AoD-IP, a novel framework for Vision-Language Models that enhances intellectual property protection by introducing a lightweight dynamic authorization module and a dual-path inference mechanism to enable flexible, user-controlled, legality-aware domain adaptation in dynamic environments.
EvoTool is a self-evolving framework that optimizes modular LLM agent tool-use policies through a gradient-free evolutionary process featuring blame-aware mutation and diversity-aware selection, significantly outperforming existing baselines in accuracy, efficiency, and transferability.
This paper introduces AgentSCOPE, a benchmark and Privacy Flow Graph framework that reveals how agentic systems frequently violate contextual privacy at intermediate pipeline stages—particularly during tool responses—demonstrating that current output-focused evaluations significantly underestimate the true privacy risks of multi-step AI workflows.